Capability $\neq$ Interpretability: Human Interpretability of Vision Foundation Models
How interpretable are the features of leading vision models? The question is increasingly pressing as these models move from research benchmarks into high-stakes deployments, yet existing methods cannot answer it reliably. We address this gap with a framework for measuring and comparing the human interpretability of vision models, built around two complementary psychophysics protocols: (1) localizability---can an observer predict where a feature fires on a novel image?---and (2) semanticity---can an observer accurately describe what the feature represents? Features are recovered via sparse autoencoders, and a chance-anchored scoring function places every model on a common scale. Applying ...
| Authors | Julien Colin, Lore Goetschalckx, Nuria Oliver et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 May 2026 |
| Last updated at source | 14 October 2026 |
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